How to use the lightgbm.plot_split_value_histogram function in lightgbm

def test_plot_split_value_histogram(self):
        gbm0 = lgb.train(self.params, self.train_data, num_boost_round=10)
        ax0 = lgb.plot_split_value_histogram(gbm0, 27)
        self.assertIsInstance(ax0, matplotlib.axes.Axes)
        self.assertEqual(ax0.get_title(), 'Split value histogram for feature with index 27')
        self.assertEqual(ax0.get_xlabel(), 'Feature split value')
        self.assertEqual(ax0.get_ylabel(), 'Count')
        self.assertLessEqual(len(ax0.patches), 2)

        gbm1 = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, silent=True)
        gbm1.fit(self.X_train, self.y_train)

        ax1 = lgb.plot_split_value_histogram(gbm1, gbm1.booster_.feature_name()[27], figsize=(10, 5),
                                             title='Histogram for feature @index/name@ @feature@',
                                             xlabel='x', ylabel='y', color='r')
        self.assertIsInstance(ax1, matplotlib.axes.Axes)
        self.assertEqual(ax1.get_title(),
                         'Histogram for feature name {}'.format(gbm1.booster_.feature_name()[27]))
        self.assertEqual(ax1.get_xlabel(), 'x')

def test_plot_split_value_histogram(self):
        gbm0 = lgb.train(self.params, self.train_data, num_boost_round=10)
        ax0 = lgb.plot_split_value_histogram(gbm0, 27)
        self.assertIsInstance(ax0, matplotlib.axes.Axes)
        self.assertEqual(ax0.get_title(), 'Split value histogram for feature with index 27')
        self.assertEqual(ax0.get_xlabel(), 'Feature split value')
        self.assertEqual(ax0.get_ylabel(), 'Count')
        self.assertLessEqual(len(ax0.patches), 2)

        gbm1 = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, silent=True)
        gbm1.fit(self.X_train, self.y_train)

        ax1 = lgb.plot_split_value_histogram(gbm1, gbm1.booster_.feature_name()[27], figsize=(10, 5),
                                             title='Histogram for feature @index/name@ @feature@',
                                             xlabel='x', ylabel='y', color='r')
        self.assertIsInstance(ax1, matplotlib.axes.Axes)
        self.assertEqual(ax1.get_title(),
                         'Histogram for feature name {}'.format(gbm1.booster_.feature_name()[27]))
        self.assertEqual(ax1.get_xlabel(), 'x')
        self.assertEqual(ax1.get_ylabel(), 'y')
        self.assertLessEqual(len(ax1.patches), 2)
        for patch in ax1.patches:
            self.assertTupleEqual(patch.get_facecolor(), (1., 0, 0, 1.))  # red

        ax2 = lgb.plot_split_value_histogram(gbm0, 27, bins=10, color=['r', 'y', 'g', 'b'],
                                             title=None, xlabel=None, ylabel=None)
        self.assertIsInstance(ax2, matplotlib.axes.Axes)
        self.assertEqual(ax2.get_title(), '')
        self.assertEqual(ax2.get_xlabel(), '')

gbm1 = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, silent=True)
        gbm1.fit(self.X_train, self.y_train)

        ax1 = lgb.plot_split_value_histogram(gbm1, gbm1.booster_.feature_name()[27], figsize=(10, 5),
                                             title='Histogram for feature @index/name@ @feature@',
                                             xlabel='x', ylabel='y', color='r')
        self.assertIsInstance(ax1, matplotlib.axes.Axes)
        self.assertEqual(ax1.get_title(),
                         'Histogram for feature name {}'.format(gbm1.booster_.feature_name()[27]))
        self.assertEqual(ax1.get_xlabel(), 'x')
        self.assertEqual(ax1.get_ylabel(), 'y')
        self.assertLessEqual(len(ax1.patches), 2)
        for patch in ax1.patches:
            self.assertTupleEqual(patch.get_facecolor(), (1., 0, 0, 1.))  # red

        ax2 = lgb.plot_split_value_histogram(gbm0, 27, bins=10, color=['r', 'y', 'g', 'b'],
                                             title=None, xlabel=None, ylabel=None)
        self.assertIsInstance(ax2, matplotlib.axes.Axes)
        self.assertEqual(ax2.get_title(), '')
        self.assertEqual(ax2.get_xlabel(), '')
        self.assertEqual(ax2.get_ylabel(), '')
        self.assertEqual(len(ax2.patches), 10)
        self.assertTupleEqual(ax2.patches[0].get_facecolor(), (1., 0, 0, 1.))  # r
        self.assertTupleEqual(ax2.patches[1].get_facecolor(), (.75, .75, 0, 1.))  # y
        self.assertTupleEqual(ax2.patches[2].get_facecolor(), (0, .5, 0, 1.))  # g
        self.assertTupleEqual(ax2.patches[3].get_facecolor(), (0, 0, 1., 1.))  # b

        self.assertRaises(ValueError, lgb.plot_split_value_histogram, gbm0, 0)  # was not used in splitting

valid_sets=[lgb_train, lgb_test],
                feature_name=['f' + str(i + 1) for i in range(X_train.shape[-1])],
                categorical_feature=[21],
                evals_result=evals_result,
                verbose_eval=10)

print('Plotting metrics recorded during training...')
ax = lgb.plot_metric(evals_result, metric='l1')
plt.show()

print('Plotting feature importances...')
ax = lgb.plot_importance(gbm, max_num_features=10)
plt.show()

print('Plotting split value histogram...')
ax = lgb.plot_split_value_histogram(gbm, feature='f26', bins='auto')
plt.show()

print('Plotting 54th tree...')  # one tree use categorical feature to split
ax = lgb.plot_tree(gbm, tree_index=53, figsize=(15, 15), show_info=['split_gain'])
plt.show()

print('Plotting 54th tree with graphviz...')
graph = lgb.create_tree_digraph(gbm, tree_index=53, name='Tree54')
graph.render(view=True)